{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\lib\\requests\\__init__.py:80: RequestsDependencyWarning: urllib3 (1.25.11) or chardet (3.0.4) doesn't match a supported version!\n",
      "  RequestsDependencyWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matchzoo version 1.0.2\n",
      "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n",
      "data loading ...\n",
      "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n",
      "停用表配置成功\n"
     ]
    }
   ],
   "source": [
    "%run init.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preprocessor = mz.models.MatchPyramid.get_default_preprocessor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing text_left with chain_transform of ChineseRemoveBlack => ChineseSimplified => ChineseEmotion => IsChinese => ChineseStopRemoval => ChineseTokenizeDemo => Tokenize => Lowercase => PuncRemoval:   0%| | 0/92 [00:00<?, ?it/s]Building prefix dict from the default dictionary ...\n",
      "Loading model from cache C:\\Users\\ADMINI~1\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 1.460 seconds.\n",
      "Prefix dict has been built succesfully.\n",
      "Processing text_left with chain_transform of ChineseRemoveBlack => ChineseSimplified => ChineseEmotion => IsChinese => ChineseStopRemoval => ChineseTokenizeDemo => Tokenize => Lowercase => PuncRemoval: 100%|█| 92/92 [00:02<00:00, 43.96it/s]\n",
      "Processing text_right with chain_transform of ChineseRemoveBlack => ChineseSimplified => ChineseEmotion => IsChinese => ChineseStopRemoval => ChineseTokenizeDemo => Tokenize => Lowercase => PuncRemoval: 100%|█| 99/99 [00:01<00:00, 93.27it/s] \n",
      "Processing text_right with append: 100%|████████████████████████████████████████████| 99/99 [00:00<00:00, 32986.66it/s]\n",
      "Building FrequencyFilter from a datapack.: 100%|████████████████████████████████████| 99/99 [00:00<00:00, 24886.79it/s]\n",
      "Processing text_right with transform: 100%|█████████████████████████████████████████| 99/99 [00:00<00:00, 32999.77it/s]\n",
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      "Processing text_right with extend: 100%|████████████████████████████████████████████| 99/99 [00:00<00:00, 12382.17it/s]\n",
      "Building Vocabulary from a datapack.: 100%|██████████████████████████████████████| 805/805 [00:00<00:00, 805250.35it/s]\n",
      "Processing text_left with chain_transform of ChineseRemoveBlack => ChineseSimplified => ChineseEmotion => IsChinese => ChineseStopRemoval => ChineseTokenizeDemo => Tokenize => Lowercase => PuncRemoval: 100%|█| 92/92 [00:00<00:00, 146.82it/s]\n",
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     ]
    }
   ],
   "source": [
    "train_pack_processed = preprocessor.fit_transform(train_pack_raw)\n",
    "dev_pack_processed = preprocessor.transform(dev_pack_raw)\n",
    "test_pack_processed = preprocessor.transform(test_pack_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'embedding_input_dim': 308,\n",
       " 'filter_unit': <mzcn.preprocessors.units.frequency_filter.FrequencyFilter at 0x25e5bd00b38>,\n",
       " 'vocab_size': 308,\n",
       " 'vocab_unit': <mzcn.preprocessors.units.vocabulary.Vocabulary at 0x25e699fb630>}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessor.context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# glove_embedding = mz.datasets.embeddings.load_glove_embedding(dimension=300)\n",
    "# term_index = preprocessor.context['vocab_unit'].state['term_index']\n",
    "# embedding_matrix = glove_embedding.build_matrix(term_index)\n",
    "# l2_norm = np.sqrt((embedding_matrix * embedding_matrix).sum(axis=1))\n",
    "# embedding_matrix = embedding_matrix / l2_norm[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainset = mz.dataloader.Dataset(\n",
    "    data_pack=train_pack_processed,\n",
    "    mode='pair',\n",
    "    num_dup=2,\n",
    "    num_neg=1\n",
    ")\n",
    "devset = mz.dataloader.Dataset(\n",
    "    data_pack=dev_pack_processed\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "padding_callback = mz.models.MatchPyramid.get_default_padding_callback()\n",
    "\n",
    "trainloader = mz.dataloader.DataLoader(\n",
    "    dataset=trainset,\n",
    "    stage='train',\n",
    "    callback=padding_callback\n",
    ")\n",
    "devloader = mz.dataloader.DataLoader(\n",
    "    dataset=devset,\n",
    "    stage='dev',\n",
    "    callback=padding_callback\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MatchPyramid(\n",
      "  (embedding): Embedding(308, 100, padding_idx=0)\n",
      "  (matching): Matching()\n",
      "  (conv2d): Sequential(\n",
      "    (0): Sequential(\n",
      "      (0): ConstantPad2d(padding=(0, 2, 0, 2), value=0)\n",
      "      (1): Conv2d(1, 16, kernel_size=[3, 3], stride=(1, 1))\n",
      "      (2): ReLU()\n",
      "    )\n",
      "    (1): Sequential(\n",
      "      (0): ConstantPad2d(padding=(0, 2, 0, 2), value=0)\n",
      "      (1): Conv2d(16, 32, kernel_size=[3, 3], stride=(1, 1))\n",
      "      (2): ReLU()\n",
      "    )\n",
      "  )\n",
      "  (dpool_layer): AdaptiveAvgPool2d(output_size=[3, 10])\n",
      "  (dropout): Dropout(p=0.1, inplace=False)\n",
      "  (out): Linear(in_features=960, out_features=1, bias=True)\n",
      ")\n",
      "Trainable params:  36561\n"
     ]
    }
   ],
   "source": [
    "model = mz.models.MatchPyramid()\n",
    "\n",
    "model.params['task'] = ranking_task\n",
    "# model.params['embedding'] = embedding_matrix #这里是当加载glove等模型时取消该行注释\n",
    "#设置embedding系数\n",
    "model.params[\"embedding_output_dim\"]=100\n",
    "model.params[\"embedding_input_dim\"]=preprocessor.context[\"embedding_input_dim\"]\n",
    "model.params['kernel_count'] = [16, 32]\n",
    "model.params['kernel_size'] = [[3, 3], [3, 3]]\n",
    "model.params['dpool_size'] = [3, 10]\n",
    "model.params['dropout_rate'] = 0.1\n",
    "\n",
    "model.build()\n",
    "\n",
    "print(model)\n",
    "print('Trainable params: ', sum(p.numel() for p in model.parameters() if p.requires_grad))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam(model.parameters())\n",
    "\n",
    "trainer = mz.trainers.Trainer(\n",
    "    model=model,\n",
    "    optimizer=optimizer,\n",
    "    trainloader=trainloader,\n",
    "    validloader=devloader,\n",
    "    validate_interval=None,\n",
    "    epochs=5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11e2d810a615469d8738d33ad080d425",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Iter-1 Loss-0.000]:\n",
      "  Validation: normalized_discounted_cumulative_gain@3(0.0): 0.1579 - normalized_discounted_cumulative_gain@5(0.0): 0.1579 - mean_average_precision(0.0): 0.1579\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "68cbd43edd954320b7319a361c1915ed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Iter-2 Loss-0.000]:\n",
      "  Validation: normalized_discounted_cumulative_gain@3(0.0): 0.1579 - normalized_discounted_cumulative_gain@5(0.0): 0.1579 - mean_average_precision(0.0): 0.1579\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "24064f13207243ccb692e1a178c9000a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Iter-3 Loss-0.000]:\n",
      "  Validation: normalized_discounted_cumulative_gain@3(0.0): 0.1579 - normalized_discounted_cumulative_gain@5(0.0): 0.1579 - mean_average_precision(0.0): 0.1579\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7203f54f888c4917b06822d24441bd7f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Iter-4 Loss-0.000]:\n",
      "  Validation: normalized_discounted_cumulative_gain@3(0.0): 0.1579 - normalized_discounted_cumulative_gain@5(0.0): 0.1579 - mean_average_precision(0.0): 0.1579\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "51fb951ea16d4ec78ea157f826339c35",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Iter-5 Loss-0.000]:\n",
      "  Validation: normalized_discounted_cumulative_gain@3(0.0): 0.1579 - normalized_discounted_cumulative_gain@5(0.0): 0.1579 - mean_average_precision(0.0): 0.1579\n",
      "\n",
      "Cost time: 1.588088035583496s\n"
     ]
    }
   ],
   "source": [
    "trainer.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "375"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gc\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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